Intelligence Analysts

Gather, analyze, or evaluate information from a variety of sources, such as law enforcement databases, surveillance, intelligence networks or geographic information systems. Use intelligence data to anticipate and prevent organized crime activities, such as terrorism.

The occupation of Intelligence Analysts has an automation risk of 48.0%, which is moderately high but not entirely automatable. This reflects a base risk of 48.8%, indicating that nearly half of the tasks in this job could potentially be performed by automated systems or artificial intelligence in the foreseeable future. Many of the daily responsibilities of intelligence analysts involve data-heavy and pattern-recognition activities, areas where AI and machine learning have shown great advances. However, the remaining portion of the role still requires uniquely human abilities such as creative analysis and contextual judgment, causing the overall automation risk to remain below 50%. Thus, while automation technologies are likely to transform this occupation substantially, human oversight and input will continue to be necessary. The tasks most susceptible to automation within intelligence analysis are those that involve structured data processing and verification. For example, "Validate known intelligence with data from other sources" can often be handled by algorithms trained to cross-reference and corroborate multiple databases rapidly. Similarly, "Gather, analyze, correlate, or evaluate information from a variety of resources, such as law enforcement databases" is amenable to automation, as it relies heavily on data mining and integration techniques now commonplace in AI-driven software. Finally, "Evaluate records of communications, such as telephone calls, to plot activity and determine the size and location of criminal groups and members" can be efficiently executed with network analysis algorithms and speech-to-text technologies, reducing the need for manual review. Conversely, the tasks most resistant to automation rely heavily on human judgment, adaptability, and domain-specific expertise. Operating cameras, radios, or other surveillance equipment often requires real-time decision-making and improvisation, qualities difficult to replicate with machines. Furthermore, gathering and evaluating information using tools such as aerial photographs or radar equipment typically requires nuanced interpretation and field-specific discretion. The task of studying communication code languages or foreign languages to translate intelligence is especially challenging for machines; while translation engines have advanced, subtle nuances, idioms, and contextual meanings still demand human involvement. Bottleneck skills like Originality—measured at 3.1% and 3.5%—underscore this resistance: creative and original thinking, essential to advanced intelligence work, remains an area where machines lag behind skilled analysts.